End-user composition of interactive applications through actionable UI components

Ordinary web users can now create and publish online content. They even venture into “mashups,” integrating information from different sources into a composite information-providing web service. This is a non-trivial design task, which falls into the area of end-user development when the ordinary users who perform it do not have programming education. In this article, we investigate the service design strategies of 12 such ordinary users and compare them against the baseline of 12 programmers. In our think-aloud study, users completed two contrasting types of tasks involved in developing service-based applications: (a) manual service composition and (b) parametric design using templates with a high degree of software support (or assisted composition). These service composition tasks were chosen to differ in respect to the level of user support provided by the tool. Our findings show that non-programmers liked, more than programmers, the template-based parametric design and did not find the tool assistance as constraining as the programmers did. The difficulty of design involved in manual service composition and the absence of user guidance hindered non-programmers in expressing and implementing accurate design solutions. The differences in the mental models and needs of non-programmers are established to be in stark contrast to those of programmers. We used the details of our findings to propose specialized design recommendations for service composition tools aligned with the profiles of their target users.

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“…H1c supported). This result agrees with various findings, such as [11,71]. Such understanding and associations are beyond ordinary people.…”

Section: Discussion and Practical Implications For Service Compositionsupporting

confidence: 93%

Non-Programmers Composing Software Services: A Confirmatory Study of the Mental Models and Design Challenges

2019

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“…After creating the project, the product was hosted on a web server and the URL was given to all participants. We sent an invitation email to the 19 participants and asked them to evaluate the DDKAT directly based on their judgments [65]. In the email, we mentioned all the features of the DDKAT, its usage instructions, the actual product's URL, and the eSURVEY URL for the AttrakDiff questionnaire.…”

Section: B Experimental Executionmentioning

confidence: 99%

“…7, in which PQ, HQ-I, HQ-S, and ATT were rated by computing their average scores, as shown on the y-axis. PQ represents the usability of the product and reveals how well users are accomplishing their objectives; HQ-S describes the supporting features of a system, such as its novelty, interesting and stimulating functions, contents, and presentation styles; HQ-I indicates the ability of users to identify with the system; and ATT represents the overall perceived quality of the system [65]. The user assessments of all four dimensions fell between 1 and 2 for the DDKAT, which is in the ''above-average'' region.…”

Section: Expert Evaluationsmentioning

confidence: 99%

“…Figure 7 shows the average scores of the four AttrakDiff dimensions, while a detailed view of the ratings for these four dimensions is provided in Fig. 8, which shows the AttrakDiff adjective-pair questions [65]. For the PQ and ATT dimensions of the DDKAT, the average score was 6 out of 7 for all adjective-pairs except ''technical human.''…”

Section: Expert Evaluationsmentioning

confidence: 99%

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A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

Ali

Khan

et al. 2018

IEEE Access

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Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors. INDEX TERMSKnowledge engineering, data mining, features ranking, algorithm selection, decision tree, production rule, user experience. I. INTRODUCTIONKnowledge systems have come a long way, from manual knowledge curation to automatic data-driven knowledge generation. The major drivers of this transition were the size and complexity of data. Since large datasets cannot be efficiently analyzed manually, the automation process is essential [2].Initially in this process of knowledge automation, knowledge engineers followed ad-hoc procedures [3]. Later on, more systematic methodologies were devised, which can be referred to as data-driven knowledge acquisition systems.Knowledge extraction from structured sources such as databases is an active area of research in the information

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“…In the software development industry, the experience of a good user refers to an evaluation of an end user interface that is easy to learn and efficient to use (aesthetics, joy of use, and attractiveness). However, UI design management is highly concerned that developers spend more time designing UI (Desolda, Ardito, Costabile, & Matera, 2017;Kennard & Leaney, 2010). Hays (2014) claims that complex user interfaces can encounter a delayed reaction time and unsatisfied users.…”

Section: Introductionmentioning

confidence: 99%

A Practical Model From Multidimensional Layering: Personal Finance Information Framework Using Mobile Software Interface Operations

Rukhiran

Netinant

2020

Journal of Information and Communication Technology

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End user involvement is crucial in improving software development processes. Hence, nowadays user interface (UI) and user experience (UX) are particularly concerned with end user interactions in many software designs as most methodologies have inconsistencies between design and implementation. Besides, it is relatively difficult to make changes in complex software and personal finance application is one of the more complex software to design, develop, and adapt. This paper proposes the development of a mobile personal finance application using informative multidimensional layering. We have separated functional data cutting across the relationships of three categories and datasets showing operational semantics of dimensions, and combined layers of three-dimensional information including aspect elements through components. This study is concerned with the corresponsive composition of end user features using visual interfaces. It is illustrated in a Three-layer User Interface Composition Model to transfer and compose layers, functional data, aspect elements, and components to Graphical User Interfaces (GUIs). Therefore, an integrated view of the software system would make the design and implementation consistent to support our framework in a more straightforward manner.

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End-user composition of interactive applications through actionable UI components

Cited by 13 publications

References 28 publications

Non-Programmers Composing Software Services: A Confirmatory Study of the Mental Models and Design Challenges

Non-Programmers Composing Software Services: A Confirmatory Study of the Mental Models and Design Challenges

A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

A Practical Model From Multidimensional Layering: Personal Finance Information Framework Using Mobile Software Interface Operations

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